▍1. recv
组播接收端，socket通信，Windows网络编程(Multicast receiver, socket communications, Windows network programming)
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▍2. fuzzy_control_C
说明： 基于模糊原理的温度控制演示程序，C++编写，完整的模糊控制算法(Fuzzy theory based temperature control demonstration program, the preparation of C, a complete fuzzy control algorithm)
▍3. telnet
自己实现的telnet的服务器端和客户端源码，VC2010实现(Their implementation of Telnet server and client source code, VC2010 implementation)
▍4. IPPackCap
编制程序，监控网络，捕获一段时间内网络上的IP数据包，按IP数据包的源地址统计出该源地址在该时间段内发出的IP包的个数，将其写入日志文件中或用图形表示出来（建议用图形表示出统计结果）。(Program monitoring network, a period of time to capture IP data packets on the network, according to the source address of the IP packet statistics of the number of the source address is issued in the time of the IP package, written into the log file or graph out (that show the statistical results with graphics).)
▍7. POP3_EXAMPLE
Pop3客户端接收信件的整个流程： 1。连接到pop3服务器（默认端口110） 2。发送用户名（user） 3。发送密码（pass） 4。查询当前邮箱状态（stat） 5。查询服务器上所有邮件UID列表（UIDL） 6。查询本地所有邮件的UID列表，从而罗列出新邮件列表（本地UID列表是POP3客户端记录在日志文件中的） 7。对所有新邮件查询其大小（LIST） 8。下载所有新邮件（RETR）利用List提供的邮件大小信息 9。如果有必要，对所有新邮件标志为删除（DELE） 10。结束对话（QUIT） 11。解析本地下载邮件。(Pop3 client receives the entire flow of letters: 1. Connect to the pop3 server (default port 110) 2. Send user name (user) 3. Send password (pass) 4. Query current mail status (stat) 5. Query server UID list of all messages (UIDL) 6. Query local UID list of all messages, which set out the new mailing list (local UID is a POP3 client list recorded in the log file) 7. For all new e-mail inquiries about their size (LIST) 8. Download all new messages (RETR) to use List of information provided by message size 9. If necessary, for all new signs for the delete message (DELE) 10. The end of the dialogue (QUIT) 11. Analysis of local download your mail.)
▍8. mvb
列车MVB网络开发的C程序,对于初学者了解列车网络通讯，为进一步开发列车网络控制系统具有借鉴。(it is a C program about vehicle mvb network)
▍9. FunctionApproximation
使用C++实现BP神经网络拟合函数f(x)=sin(x)+cos(x)(Using C++ to achieve BP neural network fitting function f (x) = sin (x)+ cos (x))
▍10. FunctionApproximation
使用C++实现BP神经网络拟合函数f(x)=sin(x)+cos(x)(Using C++ to achieve BP neural network fitting function f (x) = sin (x)+ cos (x))
▍11. QLocalSocketDemo
实现了两个客户机程序与服务器程序间的进程通信(本机)。客户机与服务器间的通信是双向的，采用QLocalServer与QLocalSocket实现。(The realization of the two procedures and the client server inter-process communication (local). Client and server communication is a two-way, using QLocalServer with QLocalSocket achieve.)
▍12. dongwushibiesitong
动物识别系统,是人工智能的作业,同学们可以共享一下(Animal Identification System is operating in artificial intelligence, students can share what)
▍13. Robot-inversion-movement
设计一个用于解决两关节机械手的反演运动学问题的前馈网络，要求末端执行器的运动轨迹是一条直线.(Design a feedforward network is used to solve the two-joint robot inverse kinematics problem, requirements of the end-effector trajectory is a straight line.)
▍14. FPGA
说明： 实现人脸识别，通过硬件FPGA来进行实现(Realize face recognition, realize face recognition by hardware FPGA)
▍15. knn
k-nn算法实现分类，模式识别作业，分三类，75个训练集，25个测试集，输出对测试集分类的结果(k-nn classification algorithm, pattern recognition operations are classified into three types, 75 training sets, 25 test set, the output of the test set classification results)
▍16. TSP
说明： 使用蚁群算法编写的C++环境下的旅行商（TSP）经典问题的解决方法(Ant colony algorithm using C++ environment, prepared under the traveling salesman (TSP) classical solution to the problem)
▍17. VS2005-IPAddressControlLib-rev-10
VS很好用的IP类文件，外国人编写的哦，很实用的命名空间(IP class files VS good use of foreigners prepared oh， Very practical namespace)
▍18. jinziqi
说明： 人工智能作业，人机对弈模式的井字棋（三字棋，一字棋），采用αβ剪枝算法，有比较高的的搜索效率。(Intelligence operations, human-computer mode crosswise Chess Playing Chess (Chess words, word Chess), with αβ pruning algorithm has higher search efficiency.)
▍19. Genetic_Algorithm_for_Mapping
采用遗传算法来实现任务图到多核处理器的映射功能。遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。这个程序通过遗传算法那实现任务图到多核的映射的路径最优。(Using genetic algorithms to achieve the task graph mapping to multicore processors . Genetic algorithms are used in the field of artificial intelligence in computer science to solve an optimization heuristic search algorithm , an evolutionary algorithm . This heuristic is typically used to generate a useful solution to optimization and search problems. Evolutionary algorithm was originally borrowed from evolutionary biology and developed a number of phenomena , these phenomena include genetic mutation , natural selection and hybridization . Genetic algorithms are typically implemented as a computer simulation methods . For an optimization problem , a number of candidate solutions ( called individuals ) an abstract representation of the population ( called chromosomes ) to evolve a better solution . Traditionally, the solution of the binary representation ( i.e., a string of 0 and 1 ) , but can also be expressed by other methods . Evolved from a completely random population of individua)
▍20. Genetic_Algorithm_for_Mapping
采用遗传算法来实现任务图到多核处理器的映射功能。遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。这个程序通过遗传算法那实现任务图到多核的映射的路径最优。(Using genetic algorithms to achieve the task graph mapping to multicore processors . Genetic algorithms are used in the field of artificial intelligence in computer science to solve an optimization heuristic search algorithm , an evolutionary algorithm . This heuristic is typically used to generate a useful solution to optimization and search problems. Evolutionary algorithm was originally borrowed from evolutionary biology and developed a number of phenomena , these phenomena include genetic mutation , natural selection and hybridization . Genetic algorithms are typically implemented as a computer simulation methods . For an optimization problem , a number of candidate solutions ( called individuals ) an abstract representation of the population ( called chromosomes ) to evolve a better solution . Traditionally, the solution of the binary representation ( i.e., a string of 0 and 1 ) , but can also be expressed by other methods . Evolved from a completely random population of individua)
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